Cue onset dummy coding

Table of Contents

Pain > (Vicarious & Cognitive)

Pain > VC :: load dataset

clear all;
close all;
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0010.nii'));
spm('Defaults','fMRI')
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0010.nii'
loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 38736756 bytes Loading image number: 97 Elapsed time is 3.979020 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 9137299 Bit rate: 23.12 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
'stim_P', 'stim_V', 'stim_C', 'stim_G',...
'motor', ...
'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

Pain > VC :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
SPM12: spm_check_registration (v7759) 11:26:47 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions

Pain > VC :: Plot diagnostics, before l2norm

drawnow; snapnow
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 34.02% Expected 4.85 outside 95% ellipsoid, found 9 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 63 Uncorrected: 9 images Cases 17 27 31 59 60 63 79 93 96 Retained 8 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 41.24% Expected 4.85 outside 95% ellipsoid, found 9 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 9 images Cases 5 26 51 54 63 80 85 88 94 Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 1 1.0309 global_mean_to_variance 1 1.0309 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 0 0 mahal_cov_uncor 9 9.2784 mahal_cov_corrected 1 1.0309 mahal_corr_uncor 9 9.2784 mahal_corr_corrected 0 0 Overall_uncorrected 17 17.526 Overall_corrected 1 1.0309
SPM12: spm_check_registration (v7759) 11:27:37 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions

Pain > VC :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Pain > VC :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 97
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 1 participants, size is now 96
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
participants that are outliers:... sub-0091
disp(n);
sub-0091

Pain > VC :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Pain > VC :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
SPM12: spm_check_registration (v7759) 11:27:42 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
drawnow, snapnow;
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.038874 Image 1 21 contig. clusters, sizes 1 to 77446 Positive effect: 67482 voxels, min p-value: 0.00000000 Negative effect: 10146 voxels, min p-value: 0.00000000
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 11:27:44 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
drawnow, snapnow;
fdr_t = threshold(t, .001, 'fdr');
Image 1 FDR q < 0.001 threshold is 0.000628 Image 1 27 contig. clusters, sizes 1 to 62389 Positive effect: 56795 voxels, min p-value: 0.00000000 Negative effect: 5968 voxels, min p-value: 0.00000000
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 11:27:45 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
drawnow, snapnow;
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects
sagittal montage: 1496 voxels displayed, 61267 not displayed on these slices
sagittal montage: 1529 voxels displayed, 61234 not displayed on these slices
sagittal montage: 1488 voxels displayed, 61275 not displayed on these slices
axial montage: 11547 voxels displayed, 51216 not displayed on these slices
axial montage: 12833 voxels displayed, 49930 not displayed on these slices
drawnow, snapnow;

Pain > VC :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ ________________ -0.30527 {'anxiety' } 0.42785 {'visual' } -0.29212 {'trait' } 0.37692 {'object' } -0.28263 {'disorder' } 0.36652 {'objects' } -0.28227 {'age' } 0.33 {'attention' } -0.27596 {'sensation' } 0.32307 {'solving' } -0.27354 {'pain' } 0.31346 {'calculation' } -0.27247 {'rating' } 0.30204 {'orthographic'} -0.2665 {'stimulation'} 0.30026 {'rotation' } -0.25983 {'affect' } 0.29613 {'shape' } -0.25507 {'affective' } 0.28397 {'numerical' }

Pain > VC :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.1587 -28.9714 0.0000 1.0000 Cog Wholebrain 0.0291 9.8305 0.0000 1.0000 Emo Wholebrain 0.1242 25.8070 0.0000 1.0000
axis image
subplot(1, 3, 2)
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.15871 0.0053934 -29.426 2.2204e-15 -2.9877 {'Cog Wholebrain' } 0.02911 0.0029595 9.8362 2.2204e-15 0.99872 {'Emo Wholebrain' } 0.12416 0.0047657 26.054 2.2204e-15 2.6453
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {97×3 cell} text_han: {97×3 cell} point_han: {97×3 cell} star_handles: [11.0010 12.0010 13.0010]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
clear csim
for i = 1:3
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
subplot(1, 3, 3)
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.144 0.0051953 -27.717 2.2204e-15 -2.8142 {'Cog Wholebrain' } 0.027826 0.0026928 10.334 2.2204e-15 1.0492 {'Emo Wholebrain' } 0.11237 0.0046643 24.092 2.2204e-15 2.4461
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {97×3 cell} text_han: {97×3 cell} point_han: {97×3 cell} star_handles: [14.0010 15.0010 16.0010]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')

Vicarious > (Pain & Cognitive)

Vicarious > PC :: load dataset

clear all;
close all;
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0011.nii'));
spm('Defaults','fMRI');
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0011.nii'
loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 38736756 bytes Loading image number: 97 Elapsed time is 4.136019 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 9195733 Bit rate: 23.13 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
'stim_P', 'stim_V', 'stim_C', 'stim_G',...
'motor', ...
'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

Vicarious > PC :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
SPM12: spm_check_registration (v7759) 11:28:48 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
ans = 1×1 cell array
{1×1 region}

Vicarious > PC :: Plot diagnostics, before l2norm

drawnow; snapnow;
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 37.11% Expected 4.85 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 60 93 Uncorrected: 8 images Cases 1 11 24 30 37 60 93 97 Retained 13 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 45.36% Expected 4.85 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 88 Uncorrected: 8 images Cases 3 5 45 54 57 84 88 89 Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 1 1.0309 global_mean_to_variance 2 2.0619 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 2.0619 mahal_cov_uncor 8 8.2474 mahal_cov_corrected 2 2.0619 mahal_corr_uncor 8 8.2474 mahal_corr_corrected 1 1.0309 Overall_uncorrected 16 16.495 Overall_corrected 3 3.0928
SPM12: spm_check_registration (v7759) 11:29:32 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions

Vicarious > PC :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Vicarious > PC :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 97
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 94
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0088" "participants that are outliers:... sub-0123" "participants that are outliers:... sub-0129"
disp(n);
{'sub-0088'} {'sub-0123'} {'sub-0129'}

Vicarious > PC :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Vicarious > PC :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
SPM12: spm_check_registration (v7759) 11:29:38 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
drawnow, snapnow;
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.033479 Image 1 23 contig. clusters, sizes 1 to 66769 Positive effect: 46814 voxels, min p-value: 0.00000000 Negative effect: 20036 voxels, min p-value: 0.00000000
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 11:29:39 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
drawnow, snapnow;
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects
sagittal montage: 1689 voxels displayed, 65161 not displayed on these slices
sagittal montage: 1628 voxels displayed, 65222 not displayed on these slices
sagittal montage: 1589 voxels displayed, 65261 not displayed on these slices
axial montage: 12489 voxels displayed, 54361 not displayed on these slices
axial montage: 13574 voxels displayed, 53276 not displayed on these slices
drawnow, snapnow;

Vicarious > PC :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ____________ __________ ________________ -0.35201 {'rating' } 0.47936 {'visual' } -0.3485 {'trait' } 0.39651 {'object' } -0.31806 {'negative'} 0.38284 {'objects' } -0.31783 {'anxiety' } 0.32431 {'attention' } -0.30566 {'positive'} 0.32211 {'rotation' } -0.30522 {'disorder'} 0.31772 {'shape' } -0.29689 {'age' } 0.30574 {'letter' } -0.29304 {'ratings' } 0.27797 {'naming' } -0.29289 {'personal'} 0.27537 {'visuospatial'} -0.29256 {'affect' } 0.27314 {'orthographic'}

Vicarious > PC :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.1377 -26.5394 0.0000 1.0000 Cog Wholebrain 0.0205 6.5271 0.0000 1.0000 Emo Wholebrain 0.1120 22.1588 0.0000 1.0000
axis image
subplot(1, 3, 2)
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.1377 0.0051284 -26.851 2.2204e-15 -2.7263 {'Cog Wholebrain' } 0.020472 0.0031342 6.5319 3.0948e-09 0.66322 {'Emo Wholebrain' } 0.11201 0.0050191 22.317 2.2204e-15 2.2659
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {97×3 cell} text_han: {97×3 cell} point_han: {97×3 cell} star_handles: [11.0011 12.0011 13.0011]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
clear csim
for i = 1:3
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
subplot(1, 3, 3)
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.13228 0.0050706 -26.087 2.2204e-15 -2.6488 {'Cog Wholebrain' } 0.019944 0.0030017 6.6442 1.8394e-09 0.67461 {'Emo Wholebrain' } 0.10839 0.0050495 21.466 2.2204e-15 2.1795
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {97×3 cell} text_han: {97×3 cell} point_han: {97×3 cell} star_handles: [14.0011 15.0011 16.0011]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')

Cognitive > (Pain & Vicarious)

Cognitive > PV :: load dataset

clear all;
close all;
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0012.nii'));
spm('Defaults','fMRI');
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_CESO/1stLevel/sub-0013/con_0012.nii'
loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 38736756 bytes Loading image number: 97 Elapsed time is 4.062104 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 9200969 Bit rate: 23.13 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...
'stim_P', 'stim_V', 'stim_C', 'stim_G',...
'motor', ...
'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...
'simple_stim_P', 'simple_stim_V', 'simple_stim_C'};

Cognitive > PV :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans'); % display
SPM12: spm_check_registration (v7759) 11:30:40 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions

Cognitive > PV :: Plot diagnostics, before l2norm

drawnow; snapnow;
[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);
______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 35.05% Expected 4.85 outside 95% ellipsoid, found 12 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 60 Uncorrected: 12 images Cases 4 5 11 19 31 39 52 55 60 63 80 93 Retained 11 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 36.08% Expected 4.85 outside 95% ellipsoid, found 10 Potential outliers based on mahalanobis distance: Bonferroni corrected: 2 images Cases 5 54 Uncorrected: 10 images Cases 3 5 13 48 54 57 69 81 84 89 Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 3 3.0928 global_mean_to_variance 4 4.1237 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 0 0 mahal_cov_uncor 12 12.371 mahal_cov_corrected 1 1.0309 mahal_corr_uncor 10 10.309 mahal_corr_corrected 2 2.0619 Overall_uncorrected 21 21.649 Overall_corrected 3 3.0928
SPM12: spm_check_registration (v7759) 11:31:15 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions
Grouping contiguous voxels: 1 regions

Cognitive > PV :: run robfit

set(gcf,'Visible','on');
figure ('Visible', 'on');
drawnow, snapnow;

Cognitive > PV :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
current length is 97
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 94
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0017" "participants that are outliers:... sub-0082" "participants that are outliers:... sub-0088"
disp(n);
{'sub-0017'} {'sub-0082'} {'sub-0088'}

Cognitive > PV:: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Cognitive > PV :: ttest

t = ttest(imgs2);
One-sample t-test Calculating t-statistics and p-values
orthviews(t);
SPM12: spm_check_registration (v7759) 11:31:20 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
drawnow, snapnow;
fdr_t = threshold(t, .05, 'fdr');
Image 1 FDR q < 0.050 threshold is 0.034496 Image 1 17 contig. clusters, sizes 1 to 68823 Positive effect: 44648 voxels, min p-value: 0.00000000 Negative effect: 24273 voxels, min p-value: 0.00000000
orthviews(fdr_t);
SPM12: spm_check_registration (v7759) 11:31:22 - 11/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
drawnow, snapnow;
create_figure('montage'); axis off;
montage(fdr_t);
Setting up fmridisplay objects
sagittal montage: 1840 voxels displayed, 67081 not displayed on these slices
sagittal montage: 1715 voxels displayed, 67206 not displayed on these slices
sagittal montage: 1744 voxels displayed, 67177 not displayed on these slices
axial montage: 12796 voxels displayed, 56125 not displayed on these slices
axial montage: 13938 voxels displayed, 54983 not displayed on these slices
drawnow, snapnow;

Cognitive > PV :: Neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');
Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _____________ __________ _____________ -0.36135 {'trait' } 0.49413 {'visual' } -0.34741 {'rating' } 0.38947 {'object' } -0.32061 {'negative' } 0.37405 {'objects' } -0.3199 {'anxiety' } 0.32369 {'rotation' } -0.30914 {'affect' } 0.32293 {'shape' } -0.30901 {'disorder' } 0.31007 {'attention'} -0.30666 {'positive' } 0.28558 {'naming' } -0.30195 {'personal' } 0.28158 {'letter' } -0.29827 {'age' } 0.27876 {'execution'} -0.29519 {'affective'} 0.2747 {'visually' }
Cognitive > PV :: Pattern Phil
[obj, names] = load_image_set('pain_cog_emo');
Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.1324 -27.0647 0.0000 1.0000 Cog Wholebrain 0.0262 8.7622 0.0000 1.0000 Emo Wholebrain 0.1019 21.4037 0.0000 1.0000
axis image
subplot(1, 3, 2)
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.13239 0.004828 -27.421 2.2204e-15 -2.7842 {'Cog Wholebrain' } 0.026173 0.0029862 8.7645 6.6946e-14 0.8899 {'Emo Wholebrain' } 0.10189 0.0047327 21.529 2.2204e-15 2.1859
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {97×3 cell} text_han: {97×3 cell} point_han: {97×3 cell} star_handles: [11.0012 12.0012 13.0012]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
clear csim
for i = 1:3
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297
subplot(1, 3, 3)
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ __________ ________ {'Pain Wholebrain'} -0.12675 0.0046988 -26.975 2.2204e-15 -2.7389 {'Cog Wholebrain' } 0.025647 0.0028292 9.065 1.521e-14 0.92042 {'Emo Wholebrain' } 0.097847 0.0046544 21.022 2.2204e-15 2.1345
ans = struct with fields:
fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {97×3 cell} text_han: {97×3 cell} point_han: {97×3 cell} star_handles: [14.0012 15.0012 16.0012]
set(gca, 'FontSize', 14)
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
drawnow, snapnow;
% save html
pubdir = pwd;
pubfilename = 's03_PVC_cue_dummy_ROI.mlx';
p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
'format', 'html', 'outputDir', pubdir, ...
'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
htmlfile = publish(pubfilename, p);
Error using evalmxdom>instrumentAndRun (line 116)
Publishing a script that contains a publish function is not supported.

Error in evalmxdom (line 21)
[data,text,laste] = instrumentAndRun(file,cellBoundaries,imageDir,imagePrefix,options);

Error in publish